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MR Images Classification of Alzheimer's Disease Based on Deep Belief Network Method
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Background/Objectives: The purpose of this study was to classify Alzheimer’s disease (AD) patients from Normal Control (NC) patients using Magnetic Resonance Imaging (MRI). Methods/Statistical analysis: The performance evolution is carried out for 346 MR images from Alzheimer's Neuroimaging Initiative (ADNI) dataset. The classifier Deep Belief Network (DBN) is used for the function of classification. The network is trained using a sample training set, and the weights produced are then used to check the system's recognition capability. Findings: As a result, this paper presented a novel method of automated classification system for AD determination. The suggested method offers good performance of the experiments carried out show that the use of Gray Level Co-occurrence Matrix (GLCM) features and DBN classifier provides 98.26% accuracy with the two specific classes were tested. Improvements/Applications: AD is a neurological condition affecting the brain and causing dementia that may affect the mind and memory. The disease indirectly impacts more than 15 million relatives, companions and guardians. The results of the present research are expected to help the specialist in decision making process.

Publication Date
Sun Sep 30 2012
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Development of PVT Correlation for Iraqi Crude Oils Using Artificial Neural Network
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Several correlations have been proposed for bubble point pressure, however, the correlations could not predict bubble point pressure accurately over the wide range of operating conditions. This study presents Artificial Neural Network (ANN) model for predicting the bubble point pressure especially for oil fields in Iraq. The most affecting parameters were used as the input layer to the network. Those were reservoir temperature, oil gravity, solution gas-oil ratio and gas relative density. The model was developed using 104 real data points collected from Iraqi reservoirs. The data was divided into two groups: the first was used to train the ANN model, and the second was used to test the model to evaluate their accuracy and trend stability

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Publication Date
Wed Jan 01 2020
Journal Name
Ieee Access
Modified Elman Spike Neural Network for Identification and Control of Dynamic System
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Publication Date
Mon Jun 01 2020
Journal Name
Al-khwarizmi Engineering Journal
Prediction of Cutting Force in Turning Process by Using Artificial Neural Network
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Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samp

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Publication Date
Sun Dec 31 2006
Journal Name
Modern Sport
The standerd Levels of Some Physical abilities based on playing centers of basketball for youngsters
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Research,s Summary The purpose of the research was to specify the standerd Levels for results of basketball for Iraqi young sters, Becuse there werenot the standerd Levels which related to the testings abilities of the players based on plying centers specially the physical abilities, This made weakness in arrangement and putting the suitable training studies for different age stadges which suitable with game ,s requirements, besides evaluation the performance of the plyers in common and the levels of the coachs train in special according to the scientific style. The researchers depended on (8) special testings of chossen physical abilities, These testings applied on the teams, young players for sharing clubs among excellent series of basket

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Publication Date
Fri Sep 01 2023
Journal Name
Journal Of Engineering
Impact of Sulfate in the Sand on the Compressive Strength of Metakaolin-Based Geopolymer Mortar
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The advancement of cement alternatives in the construction materials industry is fundamental to sustainable development. Geopolymer is the optimal substitute for ordinary Portland cement, which produces 80% less CO2 emissions than ordinary Portland cement. Metakaolin was used as one of the raw materials in the geopolymerization process. This research examines the influence of three different percentages of sulfate (0.00038, 1.532, and 16.24) % in sand per molarity of NaOH on the compressive strength of metakaolin-based geopolymer mortar (MK-GPM). Samples were prepared with two different molarities (8M and 12M) and cured at room temperature. The best compressive strength value (56.98MPa) was recorded with 12M w

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Publication Date
Sun Jul 30 2023
Journal Name
Iraqi Journal Of Science
Verification of Phase Space Inversions Based on The Initial Conditions of the Chaotic Chen System
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     Theoretically, an eight-term chaos system is presented. The effect of changing the initial conditions values on behavior Chen system was studied. The basic dynamical properties of system are analyzed like time series, attractor, FFT spectrum, and bifurcation. Where the system appears steady state behavior at initial condition xi , yi , zi equal (0, 0, 0) respectively and it convert to quasi-chaotic at xi ,yi ,zi  equal (-0.1, 0.5,-0.6). Finally, the system become hyper chaotic at xi ,yi ,zi equal(-0.5, 0.5,-0.6 ) that can used it in many applications like secure communication.

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Publication Date
Mon Jan 01 2024
Journal Name
International Journal Of Research In Science And Technology
Encryption of Medical Image Based on Cascaded Design of AES Block Algorithm and Chaotic Map
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Security concerns in the transfer of medical images have drawn a lot of attention to the topic of medical picture encryption as of late. Furthermore, recent events have brought attention to the fact that medical photographs are constantly being produced and circulated online, necessitating safeguards against their inappropriate use. To improve the design of the AES algorithm standard for medical picture encryption, this research presents several new criteria. It was created so that needs for higher levels of safety and higher levels of performance could be met. First, the pixels in the image are diffused to randomly mix them up and disperse them all over the screen. Rather than using rounds, the suggested technique utilizes a cascad

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Publication Date
Sun Aug 01 2021
Journal Name
Journal Of Engineering
Utilization of Iraqi Metakaolin in Special Types of Concrete: A Review Based on National Researches
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Portland cement concrete is the most commonly used construction material in the world for decades. However, the searches in concrete technology are remaining growing to meet particular properties related to its strength, durability, and sustainability issue. Thus, several types of concrete have been developed to enhance concrete performance. Most of the modern concrete types have to contain supplementary cementitious materials (SCMs) as a partial replacement of cement. These materials are either by-products of waste such as fly ash, slag, rice husk ash, and silica fume or from a geological resource like natural pozzolans and metakaolin (MK). Ideally, the utilization of SCMs will enhance the concrete performance, minimize

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Publication Date
Tue Aug 01 2023
Journal Name
Baghdad Science Journal
The Effect of Electrospinning Parameters on Morphological and Mechanical Properties of PAN-based Nanofibers Membrane
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The electrospun nanofibers membranes (ENMs) have gained great attention due to their superior performance. However, the low mechanical strength of ENMs, such as the rigidity and low strength, limits their applications in many aspects which need adequate strength, such as water filtration. This work investigates the impact of electrospinning parameters on the properties of ENMs fabricated from polyacrylonitrile (PAN) solved in N, N-Dimethylformamide (DMF). The studied electrospinning parameters were polymer concentration, solution flow rate, collector rotating speed, and the distance between the needle and collector. The fabricated ENMs were characterized using scanning electron microscopy (SEM) to understand the surface morphology and es

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Publication Date
Fri Sep 01 2017
Journal Name
Food Research International
Effect and mechanism of cellulose nanofibrils on the active functions of biopolymer-based nanocomposite films
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